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• W2015015524 abstract "We consider a Cournot oligopoly game, where firms produce an homogenous good and the demand and cost functions are nonlinear. These features make the classical best reply solution difficult to be obtained, even if players have full information about their environment. We propose two different kinds of repeated games based on a lower degree of rationality of the firms, on a reduced information set and reduced computational capabilities. The first adjustment mechanism is called “Local Monopolistic Approximation” (LMA). First firms get the correct local estimate of the demand function and then they use such estimate in a linear approximation of the demand function where the effects of the competitors’ outputs are ignored. On the basis of this subjective demand function they solve their profit maximization problem. By using the second adjustment process, that belongs to a class of adaptive mechanisms known in the literature as “Gradient Dynamics” (GD), firms do not solve any optimization problem, but they adjust their production in the direction indicated by their (correct) estimate of the marginal profit. Both these repeated games may converge to a Cournot–Nash equilibrium, i.e. to the equilibrium of the best reply dynamics. We compare the properties of the two different dynamical systems that describe the time evolution of the oligopoly games under the two adjustment mechanisms, and we analyze the conditions that lead to non-convergence and complex dynamic behaviors. The paper extends the results of other authors that consider similar adjustment processes assuming linear cost functions or linear demand functions." @default.
• W2015015524 created "2016-06-24" @default.
• W2015015524 creator A5044658730 @default.
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• W2015015524 date "2006-08-01" @default.
• W2015015524 modified "2023-09-27" @default.
• W2015015524 title "Oligopoly games with nonlinear demand and cost functions: Two boundedly rational adjustment processes" @default.
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• W2015015524 doi "https://doi.org/10.1016/j.chaos.2005.08.103" @default.
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